Fast change point detection in switching dynamics using a hidden Markov model of prediction experts

1999 ◽  
Author(s):  
J. Kohlmorgen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Change Point ◽  
Hidden Markov ◽  
Change Point Detection ◽  
Switching Dynamics ◽  
Fast Change ◽  
Point Detection

scholarly journals Fast Change Point Detection on Dynamic Social Networks

2017 ◽  
Author(s):  
Yu Wang ◽  
Aniket Chakrabarti ◽  
David Sivakoff ◽  
Srinivasan Parthasarathy
Keyword(s):  
Communication Networks ◽  
Real World ◽  
Change Point ◽  
Dynamic Networks ◽  
Change Point Detection ◽  
Logical Time ◽  
Dynamic Social Networks ◽  
Fast Change ◽  
Time Stamps ◽  
Point Detection

A number of real world problems in many domains (e.g. sociology, biology, political science and communication networks) can be modeled as dynamic networks with nodes representing entities of interest and edges representing interactions among the entities at different points in time. A common representation for such models is the snapshot model - where a network is defined at logical time-stamps. An important problem under this model is change point detection. In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model. Our algorithm achieves up to 9X speedup over the state-of-the-art while improving quality on both synthetic and real world networks.

Hard-point detection of catenary based on Hidden Markov Model

2020 ◽  
Vol 64 (1-4) ◽  
pp. 701-709
Author(s):  
Shuxia Tian ◽  
Penghui Zhang ◽  
Liping Huang ◽  
Xueqian Song ◽  
Zhenmao Chen ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Three Dimensional ◽  
Coupling Model ◽  
Vertical Acceleration ◽  
Element Analysis ◽  
Acceleration Signal ◽  
Three Dimensional Modeling ◽  
Point Detection

Hard-point detection is an important content of catenary detection. In this paper, the pantograph-catenary coupling model was established firstly. Then the vertical acceleration of pantograph during operation was calculated by using three-dimensional modeling software and finite element analysis software. The acceleration signal mixed with white noise was filtered by global default threshold, and the hard-point detection feature signal was obtained. Finally, the Hidden Markov Model corresponding to each state of the hard-point was obtained by using the characteristic signal, which verified the feasibility of the Hidden Markov Model for hard-point detection.

Approximating the Variance of the Conditional Probability of the State of a Hidden Markov Model

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
David O Siegmund ◽  
Benjamin Yakir
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Change Point ◽  
Conditional Distribution ◽  
Hidden Markov ◽  
The State ◽  
Change Point Analysis ◽  
Score Statistic ◽  
Hidden Markov Chain ◽  
Point Analysis

In a hidden Markov model, one "estimates" the state of the hidden Markov chain at t by computing via the forwards-backwards algorithm the conditional distribution of the state vector given the observed data. The covariance matrix of this conditional distribution measures the information lost by failure to observe directly the state of the hidden process. In the case where changes of state occur slowly relative to the speed at which information about the underlying state accumulates in the observed data, we compute approximately these covariances in terms of functionals of Brownian motion that arise in change-point analysis. Applications in gene mapping, where these covariances play a role in standardizing the score statistic and in evaluating the loss of noncentrality due to incomplete information, are discussed. Numerical examples illustrate the range of validity and limitations of our results.

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